Facial recognition system, facial recognition server, and facial recognition method

ABSTRACT

A statistical processing is performed without performing complicated determination, so as not to repetitively include the same person. A facial feature extractor ( 32 ) in a facial recognition server ( 30 ) extracts features of facial image data including a face which is shown in an image obtained by a camera device ( 10 ) imaging an imaging area. A preservation unit ( 35 ) preserves features of facial image data including a face which has passed through the imaging area, in a pre-passing comparative original facial feature data memory ( 41 ). A statistics unit ( 37 ) performs statistical processing on the features of the facial image data, which are extracted by the facial feature extractor ( 32 ), in a case where feature having high similarity which is obtained by comparison with the features (extracted by the facial feature extractor ( 32 )) of the facial image data imaged by the camera device ( 10 ) and is equal to or greater than a predetermined value are not preserved in the pre-passing comparative original facial feature data memory ( 41 ) until a predetermined time from an imaging time point by the camera device ( 10 ).

TECHNICAL FIELD

The disclosure relates to a facial recognition system, a facialrecognition server, and a facial recognition method of recognizing aface of a person by using a video image which is imaged by a cameradevice.

BACKGROUND ART

In the related art, a customer analysis system as follows is known (forexample, see PTL 1). When the customer base of a person shown in animaging area set in a store is determined, the customer analysis systemdetermines whether or not the person is a customer as an analysistarget, based on an action pattern of the person, and excludes theperson who is determined not to be the analysis target, from theanalysis target. Thus, the customer analysis system analyzes thecustomer base of a customer who comes to a store, with high accuracy.

In the customer analysis system, for example, the imaging area is setsuch that a customer who is directed toward a seat from a guide waitingarea in the vicinity of a doorway in a store is imaged from the front. Aperson who performs an action which is different from an action ofmoving toward the seat is detected, and the detected person is excludedfrom the analysis target. Thus, a person such as a clerk, which is not acustomer is included as the analysis target, and an occurrence of asituation in which the same customer is repetitively included as theanalysis target is avoided.

CITATION LIST Patent Literature

PTL 1: Japanese Patent Unexamined Publication No. 2014-232495

SUMMARY OF THE INVENTION

However, in the configuration in PTL 1, in a case where a person doesnot take a predetermined action pattern, it is possible to exclude thisperson from the analysis target. However, complicated determinationprocessing is required for determining whether or not a person does apredetermined action pattern.

Even in a case where it can be determined that a person does apredetermined action pattern, if the same person goes to the doorway ofa store and back twice or more because of a certain business, it isconsidered that the person is repetitively counted. In the configurationin PTL 1, it is considered that, in a case where a person does not do apredetermined action pattern, the person is excluded from the analysistarget even though the person is a customer as the analysis target.

The disclosure has been made considering the above-describedcircumstances in the related art. An object of the disclosure is toprovide a facial recognition system, a facial recognition server, and afacial recognition method in which it is possible to efficiently performstatistical processing without performing complicated determination, soas not to repetitively include the same person.

According to the disclosure, there is provided a facial recognitionsystem in which a camera device and a facial recognition server areconnected to each other. The facial recognition server includes afeature extractor, a preservation unit, and a statistical processingunit. The feature extractor extracts features of facial image dataincluding the face of a person shown in video image data, based on thevideo image data obtained by the camera device imaging an imaging area.The preservation unit preserves the features of the facial image data,which are extracted by the feature extractor, in a facial featurememory. The statistical processing unit performs statistical processingby using the features of the facial image data, which are extracted bythe feature extractor, in a case where the features of facial imagedata, which has similarity which is obtained by comparison with thefeatures of the facial image data, which are extracted by the featureextractor and is equal to or greater than a predetermined value arepreserved in the facial feature memory over a predetermined time from animaging time point of the camera device.

According to the disclosure, there is provided a facial recognitionserver connected to a camera device. The facial recognition serverincludes a feature extractor, a preservation unit, and a statisticalprocessing unit. The feature extractor extracts features of facial imagedata including the face of a person shown in video image data, based onthe video image data obtained by the camera device imaging an imagingarea. The preservation unit preserves the features of the facial imagedata, which are extracted by the feature extractor, in a facial featurememory. The statistical processing unit performs statistical processingby using the features of the facial image data, which are extracted bythe feature extractor, in a case where the features of facial imagedata, which has similarity which is obtained by comparison with thefeatures of the facial image data, which are extracted by the featureextractor and is equal to or greater than a predetermined value arepreserved in the facial feature memory over a predetermined time from animaging time point of the camera device.

According to the disclosure, there is provided a facial recognitionmethod in a facial recognition system in which a camera device and afacial recognition server are connected to each other. The facialrecognition method includes processing of extracting features of facialimage data including the face of a person shown in video image data,based on the video image data obtained by the camera device imaging animaging area, processing of preserving the extracted features of thefacial image data in a facial feature memory, and processing ofperforming statistical processing by using the extracted features of thefacial image data, in a case where the features of facial image data,which has similarity which is obtained by comparison with the extractedfeatures of the facial image data and is equal to or greater than apredetermined value are preserved in the facial feature memory over apredetermined time from an imaging time point of the camera device.

According to the disclosure, it is possible to perform statisticalprocessing without performing complicated determination, so as not torepetitively include the same person.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram specifically illustrating an example of aninternal configuration of a facial recognition system according to anexemplary embodiment.

FIG. 2 is a flowchart illustrating an example of operation procedures offace detection processing in a facial recognition server in thisexemplary embodiment.

FIG. 3 is a diagram illustrating an example of a UI screen displayed ina display of a user terminal.

FIG. 4 is a block diagram specifically illustrating an example of aninternal configuration of facial recognition system in a modificationexample of this exemplary embodiment.

DESCRIPTION OF EMBODIMENT

Hereinafter, an exemplary embodiment (referred to as “this exemplaryembodiment” below) in which a facial recognition system, a facialrecognition server, and a facial recognition method in the disclosureare specifically disclosed will be described in detail with reference tothe drawings. Detailed descriptions which are unnecessary may beomitted. For example, detailed descriptions of an item which are alreadyknown or the repetitive descriptions for the substantially samecomponent may be omitted. This is because it is avoided that thefollowing descriptions become unnecessarily redundant, and the person inthe related art is caused to easily understand the followingdescriptions. The accompanying drawings and the following descriptionsare provided in order to cause the person in the related art tosufficiently understand the disclosure and it is not intended that thetopics described in claims are not limited thereto.

FIG. 1 is a block diagram specifically illustrating an example of aninternal configuration of facial recognition system 5 in this exemplaryembodiment. Facial recognition system 5 illustrated in FIG. 1 has aconfiguration in which plurality of camera devices 10, facialrecognition server 30, and user terminal 60 are connected to each other.

Each of camera devices 10 performs imaging of a predetermined locationin a store or the like, which is preset, as an imaging area. Each ofcamera devices 10 acquires facial image data including the face of aperson who passes through the imaging area. The face of the person isshown in the imaged video image. Each of camera devices 10 includesimaging unit 11, face detector 12, face clipping unit 13, andcommunication unit 14. The number of camera devices 10 in this exemplaryembodiment may be one or plural.

The imaging unit includes an image pickup device such as a chargecoupled device (CCD) image sensor or a complementary metal-oxidesemiconductor (CMOS) image sensor. The imaging unit performs imaging oflight which is incident from the preset imaging area, on a lightreceiving surface, and converts the optical image into an electricalsignal. Thus, a frame of video image data indicating a video image ofthe imaging area is obtained.

Face detector 12 detects a face included in the video image imaged byimaging unit 11. Face detection processing is processing of detecting aface by using a well-known technology, for example, as follows: a methodof detecting a part of the face, such as the eyes, the nose, and themouse; a method of detecting a skin color; a method of detecting thehair; and a method of detecting a part such as the neck and theshoulders. As the method of the face detection processing, a patternrecognition technology based on statistical learning may be used.

Face clipping unit 13 clips facial image data including the face whichhas been detected by the face detector 12, from the frame of the videoimage imaged by imaging unit 11. The clipped facial image data is datawhich includes a rectangular image having a size as large as includesthe imaged face. Face detector 12 and face clipping unit 13 arefunctions performed, for example, by processor 16 such as a centralprocessing unit (CPU), a micro processing unit (MPU), or a digitalsignal processor (DSP). Processor 16 executes, for example, anapplication program stored in an internal memory so as to realize thefunction of face detector 12 and face clipping unit 13.

Communication unit 14 is connected to facial recognition server 30 in awired or wireless manner, and transmits facial image data clipped byface clipping unit 13, to facial recognition server 30. For example, ina case where camera device 10 is a network camera, communication unit 14is capable of transmitting facial image data via an internet protocol(IP) network.

Facial recognition server 30 performs recognition by collating a faceincluded in facial image data received from each of camera devices 10with the pre-registered face. Facial recognition server 30 includescommunication unit 31, facial feature extractor 32, facial featurecomparator 33, imaging time point comparator 34, preservation unit 35,pre-passing comparative original facial feature data memory 41, andfacial image data memory 42.

Communication unit 31 receives facial image data from camera device 10and transmits a searching result (that is, statistical data) whichcorresponds to a request (for example, searching request which will bedescribed later) from user terminal 60 in accordance with the request,to user terminal 60.

Facial feature extractor 32 as an example of a feature extractorextracts features (simply referred to as “facial features” below) of aface, from facial image data received by communication unit 31. Facialfeature extraction processing is processing of extracting, for example,features such as a position of the eyeball, a positional relationshipbetween the eyes, the nose, and the mouse, and a direction in whichwrinkles are formed, by using a well-known technology.

Facial feature comparator 33 compares facial features extracted byfacial feature extractor 32 to facial features registered in pre-passingcomparative original facial feature data memory 41, and determineswhether or not similarity between the extracted facial features and theregistered facial features is equal to or higher than a predeterminedvalue. In a case where the similarity is not equal to or higher than thepredetermined value (that is, in a case where the similarity is smallerthan the predetermined value), facial feature comparator 33 determinesthe similarity to be low. A predetermined value (second threshold) whenthe similarity is determined to be low may be equal to a predeterminedvalue (first threshold) when the similarity is determined to be high ormay be lower than the first threshold.

Pre-passing comparative original facial feature data memory 41 as anexample of a facial feature memory preserves facial features in a casewhere the facial features which have been extracted by facial featureextractor 32 from facial image data transmitted from camera device 10satisfy a predetermined preservation condition. The facial features arepreserved as features of facial image data including the face of aperson who has passed through the imaging area, for a predeterminedperiod. At this time, information of an imaging time point or theimaging area (position) are preserved as attached information, inaddition to the features of the facial image data.

The predetermined period starts from an imaging time point when imagedata is obtained by imaging of camera device 10. The imaging time pointis included in a header of the image data, for example. Thepredetermined period may be randomly set or changed in accordance withan operation of user terminal 60, so as to match with the environment ofthe imaging area. For example, in a case where the imaging area is in astore such as a restaurant, the predetermined period is set to be twohours as an example. In a case where the imaging area is in an officebuilding, the predetermined period is set to be 6 to 7 hours as anexample. The predetermined period may be previously set in facialrecognition server 30, or may be set in a manner that a user such as amanager operates user terminal 60 so as to transmit the value for thepredetermined period to facial recognition server 30.

Similar to the facial feature, facial image data memory 42 preservesfacial image data of the facial features registered in pre-passingcomparative original facial feature data memory 41, in association withthe facial features, for the predetermined period. Here, if thepredetermined period elapses and thus the facial features preserved inpre-passing comparative original facial feature data memory 41 aredeleted, facial image data which has been preserved in facial image datamemory 42 and corresponds to the facial features is also simultaneouslydeleted. However, the image data may be not deleted but preserved forthe longer period (for example, one year). The image data may be notdeleted and pieces of image data as many as some amount may beaccumulated, and thus may be utilized as big data.

Imaging time point comparator 34 acquires an imaging time point offacial image data received from camera device 10 in a case where thesimilarity between the facial features extracted by facial featureextractor 32 and the facial features registered in pre-passingcomparative original facial feature data memory 41 is equal to or higherthan the predetermined value (first threshold). As described above, theimaging time point is described in header information of the facialimage data.

Imaging time point comparator 34 determines whether or not theregistered facial features are preserved in pre-passing comparativeoriginal facial feature data memory 41 from an imaging time point offacial image data having facial features extracted by facial featureextractor 32 until the predetermined time. When statistical processingwhich will be described later is performed, the predetermined time is atime which is set not to repetitively count (double-count) the sameperson. The predetermined time is set to be an adequate value inaccordance with the environment of the imaging area. For example, in acase where the predetermined period when facial features are allowed tobe preserved in pre-passing comparative original facial feature datamemory 41 is 24 hours, the predetermined time is set to be, for example,two hours which are shorter than 24 hours. The predetermined period andthe predetermined time may be the same period. In this case, facialfeatures before a predetermined time are not provided in pre-passingcomparative original facial feature data memory 41. Thus, imaging timepoint comparator 34 may be omitted.

Preservation unit 35 is connected to pre-passing comparative originalfacial feature data memory 41 and facial image data memory 42.Preservation unit 35 preserves features of facial image data inpre-passing comparative original facial feature data memory 41 andpreserves facial image data corresponding to the features, in facialimage data memory 42. As will be described later, even in a case wherestatistical processing is not performed on facial features, the facialfeatures are preserved in a case where facial image data correspondingto the facial features satisfies the preservation condition.

Facial recognition server 30 further includes age-and-gender determiner36, statistics unit 37, preservation unit 38, statistical data searchingunit 39, and age-and-gender statistical data memory 43.

Age-and-gender determiner 36 estimates the age and the gender based onthe facial features extracted by facial feature extractor 32, by using awell-known technology. The estimated age may be expressed by an agerange of a certain degree or may be expressed by a representative value.

Statistics unit 37 as an example of the statistical processing unitperforms statistical processing for the age and the gender as a targetof statistics. In the statistical processing, for example, the number offaces included in a video image imaged by camera device 10 is countedfor each age and each gender, and is registered as age-and-genderstatistical data. The statistical processing may include processing ofcalculating a ratio of each age group occupying in the population, aratio between men and women, and the like, simply in addition toprocessing of counting up the number of people (number of faces) foreach age and each gender. In addition, in the statistical processing,counting is performed so as to allow searching in accordance with theAND condition or the OR condition of searching conditions(year/month/day, period, location, age, gender, camera ID, and the like)which will be described later, and the resultant obtained by thecounting may be set as the age-and-gender statistical data. Theage-and-gender statistical data is statistical data which is classifiedby at least the age and the gender, and may be sub-classified by otherfactors. Preservation unit 38 is connected to age-and-gender statisticaldata memory 43, and thus preserves the age-and-gender statistical datawhich has been subjected to statistical processing by statistics unit37, in age-and-gender statistical data memory 43. Age-and-genderstatistical data memory 43 preserves the age-and-gender statistical datafor a long term and is used when statistical data is used.

Statistical data searching unit 39 searches for age-and-genderstatistical data memory 43 in a predetermined searching condition, inaccordance with a request from user terminal 60, and transmits asearching result to user terminal 60 as a response.

User terminal 60 is a general-purpose computer device that performsvarious settings or requests for facial recognition server 30. Userterminal 60 includes communication unit 61, controller 62, display 63,and operation unit 64. Communication unit 61 is connected to facialrecognition server 30 in a wired or wireless manner, and thus is capableof communicating with facial recognition server 30. For example,communication unit 61 is connected to facial recognition server 30 viaan IP network.

Controller 62 collectively controls an operation of user terminal 60,executes an application, and requires facial recognition server 30 toperform searching processing in accordance with an input operation of auser. Display 63 displays various types of information and displays thesearching condition, the searching result, and the like which will bedescribed later, on a UI screen. Operation unit 64 is a keyboard, amouse, or the like, and receives an input operation from a user, such asthe searching condition.

In a case where user terminal 60 is configured by a portable tabletterminal, operation unit 64 is integrated with display 63 as a touchpanel. Thus, an input operation may be directly performed on the screenof display 63.

An operation of facial recognition system 5 having the above-describedconfiguration will be described.

FIG. 2 is a flowchart illustrating an example of operation procedures offace detection processing in facial recognition server 30 in thisexemplary embodiment. Communication unit 31 in facial recognition server30 receives facial image data transmitted from camera device 10 (S1).

Facial feature extractor 32 extracts facial features from the receivedfacial image data (S2). Features such as a position of the eyeball, apositional relationship between the eyes, the nose, and the mouse, and adirection in which wrinkles are formed are extracted as the facialfeatures. Facial feature comparator 33 compares the facial featuresextracted by facial feature extractor 32 to facial features registeredin pre-passing comparative original facial feature data memory 41 (S3),and determines whether or not similarity between the extracted facialfeatures and the registered facial features is equal to or higher than apredetermined value (first threshold) (S4).

In a case where the similarity is not equal to or higher than thepredetermined value (that is, in a case where the similarity isdetermined to be low), preservation unit 35 preserves the facialfeatures extracted by facial feature extractor 32, in pre-passingcomparative original facial feature data memory 41 (S7). Age-and-genderdeterminer 36 estimates the age and the gender based on the facialfeatures extracted by facial feature extractor 32, by using a well-knowntechnology. Statistics unit 37 performs statistical processing for theage and the gender as a target of statistics (S8). In the statisticalprocessing, the number of pieces of age-and-gender statistical datawhich are estimated from the extracted facial features and areclassified for each age and each gender of a person is counted so as toincrement (count up).

Preservation unit 38 preserves age-and-gender statistical data which hasbeen subjected to statistical processing by statistics unit 37, inage-and-gender statistical data memory 43 (S9).

In a case where it is determined, in Step S4, that the similarity isequal to or higher than the predetermined value (first threshold),imaging time point comparator 34 compares an imaging time point offacial image data having facial features which have been extracted byfacial feature extractor 32, to an imaging time point of facial featuresregistered in pre-passing comparative original facial feature datamemory 41 (S5). Imaging time point comparator 34 determines whether ornot features which are preserved in pre-passing comparative originalfacial feature data memory 41 and has high similarity are preserveduntil a predetermined time (in a range of a predetermined time) from theimaging time point of the facial image data having the facial featureswhich have been extracted by facial feature extractor 32 (S6).

In a case where the imaging time point thereof is out of the range ofthe predetermined time (NO in S6), the facial features are set as facialfeatures of new facial image data. Statistics unit 37 performs theabove-described statistical processing in Step S8. In a case where it isdetermined that the imaging time point thereof is in the range of thepredetermined time in Step S6 (YES in S6), statistics unit 37 determinesthat the extracted facial features are already preserved. Thus,statistics unit 37 determines whether or not facial image data havingthe facial features satisfies the preservation condition (S10). In acase where the facial image data does not satisfy the preservationcondition (NO in S10), facial recognition server 30 discards the facialfeatures without being preserved in pre-passing comparative originalfacial feature data memory 41 (S11). In a case where the facial imagedata satisfies the preservation condition (YES in S10), preservationunit 35 preserves the facial features in pre-passing comparativeoriginal facial feature data memory 41 (S12).

Here, for example, in a case where preserving only the first image isset as the preservation condition, since the facial image datadetermined in Step S10 is not the first image, the facial featurescorresponding to the facial image data are not preserved and arediscarded. In a case where a preservation condition in which anorientation of a face included in the facial image data is directed tothe front and the face is largely shown is provided, if the facial imagedata does not satisfy the preservation condition, the facial featurecorresponding to the facial image data are discarded. In a case wherepreserving all pieces of facial image data is set as the preservationcondition, facial features are preserved regardless of image data. Thepreservation condition may be previously set in preservation unit 35 andmay be changed by setting information from user terminal 60 in themiddle of the operation.

In the processes of Steps S1 to S12, if the age-and-gender statisticaldata and the facial features are completely preserved, statistical datasearching unit 39 receives a request from user terminal 60, searches forage-and-gender statistical data registered in age-and-gender statisticaldata memory 43, in accordance with the request, and transmits thecorresponding age-and-gender statistical data to user terminal 60 as aresponse (S13). Then, facial recognition server 30 ends the mainoperation.

User terminal 60 requires facial recognition server 30 to search forage-and-gender statistical data and receives a searching result fromfacial recognition server 30. FIG. 3 is a diagram illustrating a UI(user interface) screen displayed in display 63 of user terminal 60. Thesearching condition used for searching for age-and-gender statisticaldata registered in age-and-gender statistical data memory 43 by a useris displayed on the left side of the screen in display 63.

Here, keywords such as year/month/day, a time zone (period), a location,the age, the gender, a camera ID when imaging is performed may beselected as the searching condition. A user inputs a check mark into aninput box of each searching item, and thus searching items may besearched in the AND condition or the OR condition which is preset.

If user terminal 60 transmits the searching condition set by the user,to the facial recognition server 30, statistical data searching unit 39searches for age-and-gender statistical data registered inage-and-gender statistical data memory 43, in accordance with thesearching condition. Facial recognition server 30 extracts facialfeatures of a person, which satisfy the searching condition, fromage-and-gender statistical data memory 43. Facial recognition server 30extracts facial image data which is registered in facial image datamemory 42 and is associated with the facial features, from facial imagedata memory 42. Facial recognition server 30 transmits the facialfeatures and the facial image data which have been extracted, to userterminal 60.

As a result, the facial features and the facial image data of a personwhich satisfy the searching condition are displayed on the right side ofthe screen in display 63 of user terminal 60. Here, as the searchingresult, facial features TK1, TK2, and TK3, and pieces of facial imagedata G1, G2, and G3 of three persons are displayed.

As described above, in facial recognition system 5 in this exemplaryembodiment, facial feature extractor 32 in facial recognition server 30extracts facial features of facial image data including a face which isshown in a video image obtained by camera device 10 imaging the imagingarea. Preservation unit 35 preserves facial features of facial imagedata including a face which has passed through the imaging area, inpre-passing comparative original facial feature data memory 41. In acase where facial features which has high similarity (equal to or higherthan the predetermined value) to the facial features (extracted byfacial feature extractor 32) of the facial image data imaged by cameradevice 10 are not preserved in pre-passing comparative original facialfeature data memory 41 until the predetermined time from an imaging timepoint by camera device 10, statistics unit 37 performs the statisticalprocessing on the facial features of the facial image data, which havebeen extracted by facial feature extractor 32.

Thus, facial recognition server 30 determines that the facial featureswhich are preserved at a time point close to the imaging time point andhas high similarity are facial features of the same person. Since thefacial features obtained at this imaging time point are not subjected tothe statistical processing, it is possible to efficiently performstatistical processing without performing complicated determination, soas not to repetitively include the same person. In facial recognitionsystem 5, since facial image data clipped from a video image frame incamera device 10 is transmitted to facial recognition server 30, it ispossible to reduce the volume of transmitted data, in comparison to acase where video image data is transmitted.

In a case where the facial features having high similarity are preservedin pre-passing comparative original facial feature data memory 41 untilthe predetermined time, if facial image data imaged by camera device 10does not satisfy the predetermined preservation condition, preservationunit 35 discards the facial features of the facial image data, whichhave been extracted by facial feature extractor 32. Thus, it is possibleto omit the unnecessary facial features and reduce the data volume to bepreserved.

If the facial image data imaged by camera device 10 satisfies thepredetermined preservation condition, the facial features of the facialimage data, which have been extracted by facial feature extractor 32 arepreserved in pre-passing comparative original facial feature data memory41. Thus, the volume of data of effective facial features is increasedand determination accuracy for the similarity of the facial features isimproved.

In a case where facial features which have low similarity (which doesnot reach the predetermined value (second threshold)) to the facialfeatures (extracted by facial feature extractor 32) of the facial imagedata imaged by camera device 10 are preserved in pre-passing comparativeoriginal facial feature data memory 41, statistics unit 37 performs thestatistical processing on the facial features of the facial image data,which have been extracted by facial feature extractor 32. Thus, it ispossible to perform the statistical processing without losing the faceof the first person.

The predetermined time may be set in accordance with the environment ofthe imaging area in a restaurant, a building, or the like. Thus, it ispossible to perform the statistical processing so as not to repetitivelyinclude the same person, regardless of the environment of the imagingarea.

Facial recognition server 30 searches for age-and-gender statisticaldata (features of facial image data, which are subjected to thestatistical processing) registered in age-and-gender statistical datamemory 43, in accordance with a request from user terminal (terminaldevice) 60, and transmits a searching result to user terminal 60 as aresponse. Thus, it is possible to contribute to using of a user andvarious types of utilization are expected.

Modification Example of this Exemplary Embodiment

FIG. 4 is a block diagram specifically illustrating an example of aninternal configuration of facial recognition system 5A in a modificationexample of this exemplary embodiment. Constituent components which arethe same as those in this exemplary embodiment are denoted by the samereference marks and descriptions thereof will not be repeated. Facialrecognition system 5A in the modification example in this exemplaryembodiment is different from the above-described exemplary embodiment inthat camera device 10A includes only imaging unit 11 and communicationunit 14, and only transmits image data itself imaged by imaging unit 11,to facial recognition server 30A by communication unit 14.

Facial recognition server 30A includes face detector 52 and faceclipping unit 53 in processor 40, differently from that in thisexemplary embodiment. Face detector 52 detects a face included in animage regarding image data (image) transmitted from camera device 10A,similar to face detector 12 in this exemplary embodiment. Face clippingunit 53 clips facial image data including a face which has been detectedby face detector 52, from a frame of a video image, similar to faceclipping unit 13 in this exemplary embodiment.

As described above, in facial recognition system 5A in the modificationexample in this exemplary embodiment, processing of applying a load tofacial recognition server 30A is efficiently concentrated. Thus, it ispossible to reduce the load of camera device 10A. As described above,since camera device 10A only transmits the captured image data (videoimage) to facial recognition server 30, camera device 10A is made withthe simple configuration. In addition, camera device 10A may be usedeven if camera device 10A is already provided in the imaging area. Infacial recognition system 5A in the modification example in thisexemplary embodiment, a general-purpose network camera may be also usedas camera device 10A.

Hitherto, various exemplary embodiments are described with reference tothe drawings, but the disclosure is not limited to the above-describedexamples. It is apparent that the person in the related art may supposevarious changes or modifications in the range described in claims and itis understood that those belong to the technical scope of thedisclosure.

INDUSTRIAL APPLICABILITY

The disclosure is useful because it is possible to efficiently performstatistical processing without performing complicated determination, soas not to repetitively include the same person when an image imaged by acamera device is used.

REFERENCE MARKS IN THE DRAWINGS

-   -   5, 5A FACIAL RECOGNITION SYSTEM    -   10, 10A CAMERA DEVICE    -   11 IMAGING UNIT    -   12, 52 FACE DETECTOR    -   13, 53 FACE CLIPPING UNIT    -   14 COMMUNICATION UNIT    -   16, 40, 66 PROCESSOR    -   30, 30A FACIAL RECOGNITION SERVER    -   31 COMMUNICATION UNIT    -   32 FACIAL FEATURE EXTRACTOR    -   33 FACIAL FEATURE COMPARATOR    -   34 IMAGING TIME POINT COMPARATOR    -   35, 38 PRESERVATION UNIT    -   36 AGE-AND-GENDER DETERMINER    -   37 STATISTICS UNIT    -   39 STATISTICAL DATA SEARCHING UNIT    -   41 PRE-PASSING COMPARATIVE ORIGINAL FACIAL FEATURE DATA MEMORY    -   42 FACIAL IMAGE DATA MEMORY    -   43 AGE-AND-GENDER STATISTICAL DATA MEMORY    -   60 USER TERMINAL    -   61 COMMUNICATION UNIT    -   62 CONTROLLER    -   63 DISPLAY    -   64 OPERATION UNIT    -   TK1, TK2, TK3 FACIAL FEATURE    -   G1, G2, G3 FACIAL IMAGE DATA

1. A facial recognition system comprising: a camera device; and a facialrecognition server, which is connected to the camera device, wherein thefacial recognition server comprising: a feature extractor, in operation,extracts features of facial image data including a face of a personshown in video image data, based on the video image data obtained by thecamera device imaging an imaging area, a preservation unit, inoperation, preserves the features of the facial image data, which areextracted by the feature extractor, in a facial feature memory, and astatistical processing unit, in operation, performs statisticalprocessing by using the features of the facial image data, which areextracted by the feature extractor, in a case where the features offacial image data, which has similarity to the features of the facialimage data, which are extracted by the feature extractor and is equal toor greater than a predetermined value are preserved in the facialfeature memory until a predetermined time elapses from an imaging timepoint of the camera device.
 2. The facial recognition system of claim 1,wherein the facial recognition server, in operation, discards thefeatures of the facial image data, which are extracted by the featureextractor, if the facial image data corresponding to the video imagedata which has been imaged by the camera device does not satisfy apredetermined preservation condition in a case where the features of thefacial image data, which has similarity which is obtained by comparisonwith the features of the facial image data, which are extracted by thefeature extractor and is equal to or greater than the predeterminedvalue are preserved in the facial feature memory for a period withoutexceeding the predetermined time from the imaging time point of thecamera device.
 3. The facial recognition system of claim 2, wherein thefacial recognition server, in operation, preserves the features of thefacial image data, which are extracted by the feature extractor, in thefacial feature memory in a case where the facial image datacorresponding to the video image data which has been imaged by thecamera device satisfies the predetermined preservation condition.
 4. Thefacial recognition system of claim 1, wherein the facial recognitionserver, in operation, performs the statistical processing by using thefeatures of the facial image data, which are extracted by the featureextractor, in a case where the features of the facial image data, whichhas similarity which is obtained by comparison with the features of thefacial image data, which are extracted by the feature extractor and isequal to or greater than the predetermined value are not preserved inthe facial feature memory.
 5. The facial recognition system of claim 1,wherein the predetermined time is allowed to be randomly set inaccordance with an environment around the imaging area.
 6. The facialrecognition system of claim 1, wherein the facial recognition server, inoperation, searches for the features of the facial image data, which aresubjected to the statistical processing by the statistical processingunit, in accordance with a searching request from a terminal device, andtransmits a searching result which corresponds to the searching request,to the terminal device.
 7. A facial recognition server which isconnected to a camera device, the server comprising: a featureextractor, in operation, extracts features of facial image dataincluding a face of a person shown in video image data, based on thevideo image data obtained by the camera device imaging an imaging area;a preservation unit, in operation, preserves the features of the facialimage data, which are extracted by the feature extractor, in a facialfeature memory; and a statistical processing unit, in operation,performs statistical processing by using the features of the facialimage data, which are extracted by the feature extractor, in a casewhere the features of facial image data, which has similarity which isobtained by comparison with the features of the facial image data, whichare extracted by the feature extractor and is equal to or greater than apredetermined value are preserved in the facial feature memory over apredetermined time from an imaging time point of the camera device.
 8. Afacial recognition method in a facial recognition system in which acamera device and a facial recognition server are connected to eachother, the method comprising: processing of extracting features offacial image data including the face of a person shown in video imagedata, based on the video image data obtained by the camera deviceimaging an imaging area; processing of preserving the extracted featuresof the facial image data in a facial feature memory; and processing ofperforming statistical processing by using the extracted features of thefacial image data, in a case where the features of facial image data,which has similarity which is obtained by comparison with the extractedfeatures of the facial image data and is equal to or greater than apredetermined value are preserved in the facial feature memory over apredetermined time from an imaging time point of the camera device.